Overview

Dataset statistics

Number of variables18
Number of observations565000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.4 MiB
Average record size in memory88.0 B

Variable types

NUM13
DATE2
CAT2
BOOL1

Warnings

resp_pkts is highly correlated with orig_ip_bytes and 1 other fieldsHigh correlation
orig_ip_bytes is highly correlated with resp_pktsHigh correlation
resp_ip_bytes is highly correlated with resp_pktsHigh correlation
orig_ip_bytes is highly skewed (γ1 = 594.8347597) Skewed
resp_pkts is highly skewed (γ1 = 747.2694264) Skewed
resp_ip_bytes is highly skewed (γ1 = 751.4623431) Skewed
ts has unique values Unique
service has 558291 (98.8%) zeros Zeros
orig_bytes has 418804 (74.1%) zeros Zeros
resp_bytes has 418804 (74.1%) zeros Zeros
conn_state has 538319 (95.3%) zeros Zeros
history has 8897 (1.6%) zeros Zeros
resp_pkts has 547350 (96.9%) zeros Zeros
resp_ip_bytes has 547350 (96.9%) zeros Zeros

Reproduction

Analysis started2022-05-31 04:07:28.735710
Analysis finished2022-05-31 04:09:11.895353
Duration1 minute and 43.16 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct521694
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440042.3587
Minimum0
Maximum1008747
Zeros7
Zeros (%)< 0.1%
Memory size4.3 MiB
2022-05-30T23:09:12.458922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19448.95
Q1138245.75
median416827.5
Q3712942.25
95-th percentile949397.05
Maximum1008747
Range1008747
Interquartile range (IQR)574696.5

Descriptive statistics

Standard deviation310656.968
Coefficient of variation (CV)0.7059706
Kurtosis-1.29034032
Mean440042.3587
Median Absolute Deviation (MAD)284373.5
Skewness0.2083309886
Sum2.486239326e+11
Variance9.650775174e+10
MonotocityNot monotonic
2022-05-30T23:09:12.771528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
928< 0.1%
 
278< 0.1%
 
618< 0.1%
 
807< 0.1%
 
657< 0.1%
 
2277< 0.1%
 
1417< 0.1%
 
647< 0.1%
 
427< 0.1%
 
1827< 0.1%
 
Other values (521684)564927> 99.9%
 
ValueCountFrequency (%) 
07< 0.1%
 
14< 0.1%
 
24< 0.1%
 
34< 0.1%
 
46< 0.1%
 
ValueCountFrequency (%) 
10087471< 0.1%
 
10087451< 0.1%
 
10087431< 0.1%
 
10087421< 0.1%
 
10087381< 0.1%
 

ts
Date

UNIQUE

Distinct565000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2018-05-09 15:30:31.015810
Maximum2019-07-03 14:39:13.917407
2022-05-30T23:09:13.099678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-30T23:09:13.412409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

id.orig_p
Real number (ℝ≥0)

Distinct28341
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44494.21168
Minimum3
Maximum64923
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2022-05-30T23:09:13.741046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile33704
Q142594
median43763
Q349867
95-th percentile58756
Maximum64923
Range64920
Interquartile range (IQR)7273

Descriptive statistics

Standard deviation10063.91686
Coefficient of variation (CV)0.2261848559
Kurtosis8.25123749
Mean44494.21168
Median Absolute Deviation (MAD)3638.5
Skewness-2.107018995
Sum-630574176
Variance101282422.5
MonotocityNot monotonic
2022-05-30T23:09:13.930079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4376319025033.7%
 
371961.3%
 
12365871.2%
 
1116300.3%
 
5353131< 0.1%
 
6883< 0.1%
 
2375< 0.1%
 
5965236< 0.1%
 
3356136< 0.1%
 
5673336< 0.1%
 
Other values (28331)35894063.5%
 
ValueCountFrequency (%) 
371961.3%
 
828< 0.1%
 
1116300.3%
 
2375< 0.1%
 
534< 0.1%
 
ValueCountFrequency (%) 
649231< 0.1%
 
639081< 0.1%
 
636501< 0.1%
 
624121< 0.1%
 
620271< 0.1%
 

id.resp_p
Real number (ℝ≥0)

Distinct62546
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14840.38008
Minimum0
Maximum65535
Zeros1893
Zeros (%)0.3%
Memory size2.2 MiB
2022-05-30T23:09:14.195673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q123
median7712
Q325022.25
95-th percentile59353
Maximum65535
Range65535
Interquartile range (IQR)24999.25

Descriptive statistics

Standard deviation19831.86314
Coefficient of variation (CV)1.336344692
Kurtosis0.09543816881
Mean14840.38008
Median Absolute Deviation (MAD)7689
Skewness1.23401254
Sum-205119848
Variance393302795.8
MonotocityNot monotonic
2022-05-30T23:09:14.445646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2312987323.0%
 
80806471811.5%
 
226227311.0%
 
2323421977.5%
 
9527211523.7%
 
59353110452.0%
 
12366961.2%
 
5038680.7%
 
334630.6%
 
124080.4%
 
Other values (62536)21730738.5%
 
ValueCountFrequency (%) 
018930.3%
 
124080.4%
 
23< 0.1%
 
334630.6%
 
45< 0.1%
 
ValueCountFrequency (%) 
655355< 0.1%
 
655343< 0.1%
 
655332< 0.1%
 
655324< 0.1%
 
655313< 0.1%
 

proto
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
1
356961 
2
199142 
3
 
8897
ValueCountFrequency (%) 
135696163.2%
 
219914235.2%
 
388971.6%
 
2022-05-30T23:09:14.711285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-30T23:09:14.883404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-30T23:09:15.024560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

service
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03517345133
Minimum0
Maximum6
Zeros558291
Zeros (%)98.8%
Memory size2.2 MiB
2022-05-30T23:09:15.180794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3752241544
Coefficient of variation (CV)10.66782304
Kurtosis153.0298611
Mean0.03517345133
Median Absolute Deviation (MAD)0
Skewness12.13882854
Sum19873
Variance0.140793166
MonotocityNot monotonic
2022-05-30T23:09:15.354053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
055829198.8%
 
527940.5%
 
121140.4%
 
216710.3%
 
383< 0.1%
 
442< 0.1%
 
65< 0.1%
 
ValueCountFrequency (%) 
055829198.8%
 
121140.4%
 
216710.3%
 
383< 0.1%
 
442< 0.1%
 
ValueCountFrequency (%) 
65< 0.1%
 
527940.5%
 
442< 0.1%
 
383< 0.1%
 
216710.3%
 

duration
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0
418804 
2
124766 
1
 
21430
ValueCountFrequency (%) 
041880474.1%
 
212476622.1%
 
1214303.8%
 
2022-05-30T23:09:15.946766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-30T23:09:16.103361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-30T23:09:16.243099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

orig_bytes
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3802530973
Minimum0
Maximum11
Zeros418804
Zeros (%)74.1%
Memory size2.2 MiB
2022-05-30T23:09:16.428660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.120707378
Coefficient of variation (CV)2.947266927
Kurtosis58.94515125
Mean0.3802530973
Median Absolute Deviation (MAD)0
Skewness6.986809052
Sum214843
Variance1.255985026
MonotocityNot monotonic
2022-05-30T23:09:16.576827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
041880474.1%
 
113056123.1%
 
248560.9%
 
1137550.7%
 
327580.5%
 
415780.3%
 
57000.1%
 
65280.1%
 
74600.1%
 
84190.1%
 
Other values (2)5810.1%
 
ValueCountFrequency (%) 
041880474.1%
 
113056123.1%
 
248560.9%
 
327580.5%
 
415780.3%
 
ValueCountFrequency (%) 
1137550.7%
 
10210< 0.1%
 
93710.1%
 
84190.1%
 
74600.1%
 

resp_bytes
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3984884956
Minimum0
Maximum13
Zeros418804
Zeros (%)74.1%
Memory size2.2 MiB
2022-05-30T23:09:16.771095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.33615198
Coefficient of variation (CV)3.353050326
Kurtosis68.74517775
Mean0.3984884956
Median Absolute Deviation (MAD)0
Skewness7.827920023
Sum225146
Variance1.785302114
MonotocityNot monotonic
2022-05-30T23:09:16.936853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
041880474.1%
 
113280123.5%
 
248480.9%
 
1348150.9%
 
312090.2%
 
47040.1%
 
54010.1%
 
63630.1%
 
72860.1%
 
8210< 0.1%
 
Other values (4)5590.1%
 
ValueCountFrequency (%) 
041880474.1%
 
113280123.5%
 
248480.9%
 
312090.2%
 
47040.1%
 
ValueCountFrequency (%) 
1348150.9%
 
12115< 0.1%
 
11119< 0.1%
 
10132< 0.1%
 
9193< 0.1%
 

conn_state
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08575929204
Minimum0
Maximum12
Zeros538319
Zeros (%)95.3%
Memory size2.2 MiB
2022-05-30T23:09:17.046207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4593123568
Coefficient of variation (CV)5.355831956
Kurtosis96.1565304
Mean0.08575929204
Median Absolute Deviation (MAD)0
Skewness8.015501744
Sum48454
Variance0.2109678411
MonotocityNot monotonic
2022-05-30T23:09:17.163804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
053831995.3%
 
1130202.3%
 
289131.6%
 
331510.6%
 
48110.1%
 
54480.1%
 
699< 0.1%
 
873< 0.1%
 
761< 0.1%
 
933< 0.1%
 
Other values (3)72< 0.1%
 
ValueCountFrequency (%) 
053831995.3%
 
1130202.3%
 
289131.6%
 
331510.6%
 
48110.1%
 
ValueCountFrequency (%) 
129< 0.1%
 
1131< 0.1%
 
1032< 0.1%
 
933< 0.1%
 
873< 0.1%
 

history
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.423010619
Minimum0
Maximum6
Zeros8897
Zeros (%)1.6%
Memory size2.2 MiB
2022-05-30T23:09:17.273613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7132519912
Coefficient of variation (CV)0.501227455
Kurtosis8.322740556
Mean1.423010619
Median Absolute Deviation (MAD)0
Skewness2.19857285
Sum804001
Variance0.5087284029
MonotocityNot monotonic
2022-05-30T23:09:17.367354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
134569561.2%
 
219261034.1%
 
088971.6%
 
578551.4%
 
365151.2%
 
431510.6%
 
6277< 0.1%
 
ValueCountFrequency (%) 
088971.6%
 
134569561.2%
 
219261034.1%
 
365151.2%
 
431510.6%
 
ValueCountFrequency (%) 
6277< 0.1%
 
578551.4%
 
431510.6%
 
365151.2%
 
219261034.1%
 

orig_pkts
Real number (ℝ≥0)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.62839292
Minimum0
Maximum31
Zeros29
Zeros (%)< 0.1%
Memory size2.2 MiB
2022-05-30T23:09:17.507081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.860805603
Coefficient of variation (CV)1.142725186
Kurtosis103.4378683
Mean1.62839292
Median Absolute Deviation (MAD)0
Skewness8.687582686
Sum920042
Variance3.462597493
MonotocityNot monotonic
2022-05-30T23:09:17.651440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
142794575.7%
 
312774622.6%
 
212600.2%
 
511140.2%
 
139280.2%
 
158060.1%
 
147310.1%
 
47180.1%
 
164800.1%
 
84180.1%
 
Other values (22)28540.5%
 
ValueCountFrequency (%) 
029< 0.1%
 
142794575.7%
 
212600.2%
 
312774622.6%
 
47180.1%
 
ValueCountFrequency (%) 
31267< 0.1%
 
3065< 0.1%
 
29160< 0.1%
 
28189< 0.1%
 
27131< 0.1%
 

orig_ip_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1168
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.9362726
Minimum0
Maximum6527241
Zeros29
Zeros (%)< 0.1%
Memory size2.2 MiB
2022-05-30T23:09:17.804836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q140
median60
Q376
95-th percentile180
Maximum6527241
Range6527241
Interquartile range (IQR)36

Descriptive statistics

Standard deviation9748.689215
Coefficient of variation (CV)84.08661931
Kurtosis376431.1804
Mean115.9362726
Median Absolute Deviation (MAD)20
Skewness594.8347597
Sum65503994
Variance95036941.42
MonotocityNot monotonic
2022-05-30T23:09:17.945446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6022133339.2%
 
4019038233.7%
 
18012693722.5%
 
7666231.2%
 
6841740.7%
 
5629130.5%
 
12737720.1%
 
576960.1%
 
2866860.1%
 
886020.1%
 
Other values (1158)98821.7%
 
ValueCountFrequency (%) 
029< 0.1%
 
4019038233.7%
 
5217< 0.1%
 
5629130.5%
 
576960.1%
 
ValueCountFrequency (%) 
65272411< 0.1%
 
32070311< 0.1%
 
5288171< 0.1%
 
4663021< 0.1%
 
2858851< 0.1%
 

resp_pkts
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7148619469
Minimum0
Maximum239484
Zeros547350
Zeros (%)96.9%
Memory size2.2 MiB
2022-05-30T23:09:18.117302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum239484
Range239484
Interquartile range (IQR)0

Descriptive statistics

Standard deviation319.2383419
Coefficient of variation (CV)446.5734165
Kurtosis560526.8119
Mean0.7148619469
Median Absolute Deviation (MAD)0
Skewness747.2694264
Sum403897
Variance101913.119
MonotocityNot monotonic
2022-05-30T23:09:18.264825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
054735096.9%
 
196071.7%
 
169850.2%
 
158320.1%
 
48110.1%
 
57410.1%
 
65850.1%
 
134000.1%
 
173750.1%
 
33400.1%
 
Other values (82)29740.5%
 
ValueCountFrequency (%) 
054735096.9%
 
196071.7%
 
22920.1%
 
33400.1%
 
48110.1%
 
ValueCountFrequency (%) 
2394841< 0.1%
 
71861< 0.1%
 
71291< 0.1%
 
61471< 0.1%
 
59751< 0.1%
 

resp_ip_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1208
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.2789204
Minimum0
Maximum349618679
Zeros547350
Zeros (%)96.9%
Memory size2.2 MiB
2022-05-30T23:09:18.441659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum349618679
Range349618679
Interquartile range (IQR)0

Descriptive statistics

Standard deviation465167.7451
Coefficient of variation (CV)704.5018866
Kurtosis564795.842
Mean660.2789204
Median Absolute Deviation (MAD)0
Skewness751.4623431
Sum373057590
Variance2.163810311e+11
MonotocityNot monotonic
2022-05-30T23:09:18.582267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
054735096.9%
 
7648480.9%
 
4030060.5%
 
7312090.2%
 
2164710.1%
 
25893130.1%
 
412282< 0.1%
 
164260< 0.1%
 
553206< 0.1%
 
3443195< 0.1%
 
Other values (1198)68601.2%
 
ValueCountFrequency (%) 
054735096.9%
 
4030060.5%
 
4413< 0.1%
 
483< 0.1%
 
52138< 0.1%
 
ValueCountFrequency (%) 
3496186791< 0.1%
 
38356971< 0.1%
 
23620111< 0.1%
 
10754161< 0.1%
 
3017941< 0.1%
 

label
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
1
333945 
0
231055 
ValueCountFrequency (%) 
133394559.1%
 
023105540.9%
 
2022-05-30T23:09:18.691632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

label2
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.791566372
Minimum0
Maximum8
Zeros967
Zeros (%)0.2%
Memory size4.3 MiB
2022-05-30T23:09:18.769747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q18
median8
Q38
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6943199332
Coefficient of variation (CV)0.08911172671
Kurtosis50.80597963
Mean7.791566372
Median Absolute Deviation (MAD)0
Skewness-6.232213592
Sum4402235
Variance0.4820801696
MonotocityNot monotonic
2022-05-30T23:09:18.863487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
848105785.1%
 
77421313.1%
 
549150.9%
 
321390.4%
 
215870.3%
 
09670.2%
 
1122< 0.1%
 
ValueCountFrequency (%) 
09670.2%
 
1122< 0.1%
 
215870.3%
 
321390.4%
 
549150.9%
 
ValueCountFrequency (%) 
848105785.1%
 
77421313.1%
 
549150.9%
 
321390.4%
 
215870.3%
 
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2018-05-21 20:52:40
Maximum2019-12-05 15:46:36
2022-05-30T23:09:18.972855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-30T23:09:19.082218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

Interactions

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Correlations

2022-05-30T23:09:19.207204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-30T23:09:19.448293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-30T23:09:19.682643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-30T23:09:19.907763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-05-30T23:09:20.111688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-05-30T23:09:08.665667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-30T23:09:09.479006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

df_indextsid.orig_pid.resp_pprotoservicedurationorig_bytesresp_bytesconn_statehistoryorig_pktsorig_ip_bytesresp_pktsresp_ip_byteslabellabel2starting_point
07565092018-05-13 01:13:42.015510082426192323100000116000182018-05-21 21:03:43
19261652018-05-13 21:32:02.01624798858178231021101318000182018-05-21 21:03:43
24348552018-05-11 13:19:06.0176160344662961106100000116000082018-05-21 21:03:43
310061132018-05-14 07:06:10.0317380434054723231011101318000182018-05-21 21:03:43
47571192018-05-13 01:17:11.0077478894376322653200000214000082018-05-21 21:03:43
53769302018-05-11 07:12:34.0121428974376354561200000214000082018-05-21 21:03:43
618512018-05-19 19:27:25.0775380134749422100000116000172018-05-21 20:52:40
78009692018-05-13 06:32:54.0344100005352423100000116000182018-05-21 21:03:43
81302302018-05-21 01:19:15.07135105152114221021101318000172018-05-21 20:52:40
99805042018-05-14 04:02:08.02159905440646231021101318000182018-05-21 21:03:43

Last rows

df_indextsid.orig_pid.resp_pprotoservicedurationorig_bytesresp_bytesconn_statehistoryorig_pktsorig_ip_bytesresp_pktsresp_ip_byteslabellabel2starting_point
5649905663502018-05-12 03:17:49.01044797938570231021101318000182018-05-21 21:03:43
5649912421762018-05-10 16:54:33.0404698855871423100000116000182018-05-21 21:03:43
5649924674352018-05-11 16:44:34.009152889437635396200000214000082018-05-21 21:03:43
5649937475732018-05-13 00:09:26.0452120305557823100000116000182018-05-21 21:03:43
5649941925982018-05-10 11:40:25.0133929254376359450200000214000082018-05-21 21:03:43
5649952785312018-05-10 20:46:48.00873994852198231021101318000182018-05-21 21:03:43
5649961509902018-05-21 05:42:01.8331460954357522100000116000172018-05-21 20:52:40
5649976462762018-05-12 12:10:10.0427660944813823100000116000182018-05-21 21:03:43
5649981151042018-05-10 03:29:55.01037502336617231021101318000182018-05-21 21:03:43
5649998802552018-05-13 16:02:18.02066588454067137571021101318000082018-05-21 21:03:43